Syngenta, a leading developer of crop varieties (seeds) that provide food for human and livestock consumption, is committed to bringing greater food security to an increasingly populous world by creating a transformational shift in farm productivity. Syngenta Soybean Research and Development (R&D) is leading Syngenta’s corporate plant-breeding strategy by developing and implementing a new product development model that is enabling the creation of an efficient and effective soybean breeding strategy. Key to the new strategy is the combination of advanced analytics and plant-breeding knowledge to find opportunities to increase crop productivity and optimize plant-breeding processes. Syngenta uses discrete-event and Monte Carlo simulation models to codify Syngenta Soybean R&D best practices, and uses stochastic optimization to create the best soybean breeding plans and strategically align its research efforts. As a result of using these new analytical tools, Syngenta estimates that it will save more than $287 million between 2012 and 2016.
The agricultural seed industry invests billions of dollars each year to improve our understanding of how best to unlock a seed’s full potential. This investment brings a significant benefit to agricultural customers—the farmers who grow commodity crops, such as corn, soybeans, and wheat. Commodity farmers expect new crop varieties to be adapted to local conditions and have greater genetic potential for yield. We refer to the amount of increase in the genetic potential for yield as “genetic gain.” The agricultural seed industry needs a universal, unbiased metric for genetic gain performance (GGP). Therefore, in 2010 we developed and implemented an algorithm that calculates an unbiased GGP metric that eliminates environmental factors (e.g., solar radiation, rainfall, and temperature) and is applicable at each stage of the product development pipeline. We subsequently used this metric during the variety development stage of our breeding projects to measure the impact of operational changes. We used weighted averages of GGP to retrospectively evaluate changes in genetic gain across 10 years of our breeding pipeline to quantify the benefit. We estimate that genetic gains are now 40 percent greater than the gains seen before implementation of the GGP in 2010. Our analyses show that the GGP metric has saved Syngenta approximately $250 million in varietal development costs, which would otherwise have been required to improve genetic gain by 40 percent. Syngenta scientists now use GGP to evaluate the genetic gain of all breeding projects. It serves as a valuable early-warning system. At the end of each growing season, we collect yield data and update the GGP database. This allows our scientists to perform an annual evaluation of genetic advances in each market segment. These assessments identify potential performance gaps likely to surface in the next growing season so that they can be avoided. Our successful development and deployment of a genetic gain metric is an important advance for both Syngenta and the entire agricultural industry.
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